Genes, maternal smoking and the offspring brain and body during adolescence: Design of The Saguenay Youth Study

Brain and Body Centre, University of Nottingham, Nottingham, United Kingdom.
Human Brain Mapping (Impact Factor: 5.97). 07/2007; 28(6):502-18. DOI: 10.1002/hbm.20402
Source: PubMed


The search for genes of complex traits is aided by the availability of multiple quantitative phenotypes collected in geographically isolated populations. Here we provide rationale for a large-scale study of gene-environment interactions influencing brain and behavior and cardiovascular and metabolic health in adolescence, namely the Saguenay Youth Study (SYS). The SYS is a retrospective study of long-term consequences of prenatal exposure to maternal cigarette smoking (PEMCS) in which multiple quantitative phenotypes are acquired over five sessions (telephone interview, home, hospital, laboratory, and school). To facilitate the search for genes that modify an individual's response to an in utero environment (i.e. PEMCS), the study is family-based (adolescent sibships) and is carried out in a relatively geographically isolated population of the Saguenay Lac-Saint-Jean (SLSJ) region in Quebec, Canada. DNA is acquired in both biological parents and in adolescent siblings. A genome-wide scan will be carried out with sib-pair linkage analyses, and fine mapping of identified loci will be done with family-based association analyses. Adolescent sibships (12-18 years of age; two or more siblings per family) are recruited in high schools throughout the SLSJ region; only children of French-Canadian origin are included. Based on a telephone interview, potential participants are classified as exposed or nonexposed prenatally to maternal cigarette smoking; the two groups are matched for the level of maternal education and the attended school. A total of 500 adolescent participants in each group will be recruited and phenotyped. The following types of datasets are collected in all adolescent participants: (1) magnetic resonance images of brain, abdominal fat, and kidneys, (2) standardized and computer-based neuropsychological tests, (3) hospital-based cardiovascular, body-composition and metabolic assessments, and (4) questionnaire-derived measures (e.g. life habits such as eating and physical activity; drug, alcohol use and delinquency; psychiatric symptoms; personality; home and school environment; academic and vocational attitudes). Parents complete a medical questionnaire, home-environment questionnaire, a handedness questionnaire, and a questionnaire about their current alcohol and drug use, depression, anxiety, and current and past antisocial behavior. To date, we have fully phenotyped a total of 408 adolescent participants. Here we provide the description of the SYS and, using the initial sample, we present information on ascertainment, demographics of the exposed and nonexposed adolescents and their parents, and the initial MRI-based assessment of familiality in the brain size and the volumes of grey and white matter.

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    • "We acquired MRI, puberty information, and hormone concentrations in the Saguenay Youth Study, a large community-based sample of adolescents from the Saguenay-Lac-Saint-Jean region of Québec. Details for the participant recruitment and testing procedures are described elsewhere (Pausova et al. 2007; Perrin et al. 2008). Table 1 includes the sample details for participants included in the T1W analyses by sex (number of participants, mean age and mean puberty score). "
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    ABSTRACT: Some of the known sex differences in white matter emerge during adolescence. Here, we replicate and extend our previous findings of sex differences in the structure of the corticospinal tract (Perrin et al. 2009; Hervé et al. 2009). In a large normative sample of adolescents, we observed age × sex interactions in the signal intensity of T1-weighted (T1W) images (n = 941) and in magnetization transfer ratio (MTR; n = 761); both features were inversely associated with age in males but not in females. Moreover, we hypothesized that the age-related differences in CST structure exhibited by males would be mediated by differences in puberty stage and levels of bioavailable testosterone. We confirmed this prediction using mediation analysis with bootstrapping. These findings suggest that sex differences in the CST structure observed during male adolescence may be due to multiple processes associated with puberty, including (but not limited to) the rising levels of testosterone.
    Brain Structure and Function 12/2014; DOI:10.1007/s00429-014-0956-9 · 5.62 Impact Factor
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    • "We used a family-based design, recruiting a minimum of two siblings per family; note that siblings were concordant for the exposure status in the majority of families (446/481 families; 93%). Phenotyping of the adolescents took place over several sessions (∼15 h in total) and included a number of domains detailed in Table 1 (further details in Pausova et al., 2007 and; each adolescent provided a fasting (morning) blood sam- ple. "
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    ABSTRACT: This paper provides an overview of the Saguenay Youth Study (SYS) and its parental arm. The overarching goal of this effort is to develop trans-generational models of developmental cascades contributing to the emergence of common chronic disorders, such as depression, addictions, dementia and cardio-metabolic diseases. Over the past 10 years, we have acquired detailed brain and cardio-metabolic phenotypes, and genome-wide genotypes, in 1029 adolescents recruited in a population with a known genetic founder effect. At present, we are extending this dataset to acquire comparable phenotypes and genotypes in the biological parents of these individuals. After providing conceptual background for this work (transactions across time, systems and organs), we describe briefly the tools employed in the adolescent arm of this cohort and highlight some of the initial accomplishments. We then outline in detail the phenotyping protocol used to acquire comparable data in the parents.
    Developmental Cognitive Neuroscience 10/2014; 11. DOI:10.1016/j.dcn.2014.10.003 · 3.83 Impact Factor
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    • "The gold standard for neuroanatomical image segmentation is manual delineation by an expert human rater. However, with the availability of increasingly large MRI datasets the time and expertise required for manual segmentation becomes prohibitive (Mazziotta et al., 1995, 2001; Mazziotta et al.; Pausova et al., 2007). This effort is complicated by the fact that there is significant variation between segmentation protocols with respect to specific anatomical boundaries of the hippocampus (Geuze et al., 2004) and this has led to efforts to create an unified hippocampal segmentation protocol (Boccardi et al., 2013a,b; Jack et al., 2011). "
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    ABSTRACT: Introduction: Advances in image segmentation of magnetic resonance images (MRI) have demonstrated that multi-atlas approaches improve segmentation over regular atlas-based approaches. These approaches often rely on a large number of manually segmented atlases (e.g. 30-80) that take significant time and expertise to produce. We present an algorithm, MAGeT-Brain (Multiple Automatically Generated Templates), for the automatic segmentation of the hippocampus that minimises the number of atlases needed whilst still achieving similar agreement to multi-atlas approaches. Thus, our method acts as a reliable multi-atlas approach when using special or hard-to-define atlases that are laborious to construct. Method: MAGeT-Brain works by propagating atlas segmentations to a template library, formed from a subset of target images, via transformations estimated by nonlinear image registration. The resulting segmentations are then propagated to each target image and fused using a label fusion method. We conduct two separate Monte Carlo cross-validation experiments comparing MAGeT-Brain and basic multi-atlas whole hippocampal segmentation using differing atlas and template library sizes, and registration and label fusion methods. The first experiment is a 10-fold validation (per parameter setting) over 60 subjects taken from the Alzheimer's Disease Neuroimaging Database (ADNI), and the second is a five-fold validation over 81 subjects having had a first episode of psychosis. In both cases, automated segmentations are compared with manual segmentations following the Pruessner-protocol. Using the best settings found from these experiments, we segment 246 images of the ADNI1:Complete 1Yr 1.5 T dataset and compare these with segmentations from existing automated and semi-automated methods: FSL FIRST, FreeSurfer, MAPER, and SNT. Finally, we conduct a leave-one-out cross-validation of hippocampal subfield segmentation in standard 3T T1-weighted images, using five high-resolution manually segmented atlases (Winterburn et al., 2013). Results: In the ADNI cross-validation, using 9 atlases MAGeT-Brain achieves a mean Dice's Similarity Coefficient (DSC) score of 0.869 with respect to manual whole hippocampus segmentations, and also exhibits significantly lower variability in DSC scores than multi-atlas segmentation. In the younger, psychosis dataset, MAGeT-Brain achieves a mean DSC score of 0.892 and produces volumes which agree with manual segmentation volumes better than those produced by the FreeSurfer and FSL FIRST methods (mean difference in volume: 80 mm(3), 1600 mm(3), and 800 mm(3), respectively). Similarly, in the ADNI1:Complete 1Yr 1.5 T dataset, MAGeT-Brain produces hippocampal segmentations well correlated (r>0.85) with SNT semi-automated reference volumes within disease categories, and shows a conservative bias and a mean difference in volume of 250 mm(3) across the entire dataset, compared with FreeSurfer and FSL FIRST which both overestimate volume differences by 2600 mm(3) and 2800 mm(3) on average, respectively. Finally, MAGeT-Brain segments the CA1, CA4/DG and subiculum subfields on standard 3T T1-weighted resolution images with DSC overlap scores of 0.56, 0.65, and 0.58, respectively, relative to manual segmentations. Conclusion: We demonstrate that MAGeT-Brain produces consistent whole hippocampal segmentations using only 9 atlases, or fewer, with various hippocampal definitions, disease populations, and image acquisition types. Additionally, we show that MAGeT-Brain identifies hippocampal subfields in standard 3T T1-weighted images with overlap scores comparable to competing methods.
    NeuroImage 04/2014; 101. DOI:10.1016/j.neuroimage.2014.04.054 · 6.36 Impact Factor
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